干旱区生态环境知识资源中心(Arid): ALOS融合影像质量评价及其土地盐渍化应用研究

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	ALOS融合影像质量评价及其土地盐渍化应用研究
其他题名	Quality Evaluation and Land Salinization Classification Application on ALOS Image Fusion
	张利华 1; 翟靖超 1; 李珊 1; 杨金明 2
来源期刊	地理科学
ISSN	1000-0690
出版年	2015
卷号	35 期号:6 页码:798-804
中文摘要	将经过配准的同一地区不同空间分辨率和光谱分辨率的遥感影像进行融合是提高土地覆盖/土地利用分析精度的有效途径。采用PCA、IHS、HPF和小波变换融合法对内蒙古杭锦后旗中部地区的ALOS全色和多光谱影像进行融合,并对融合结果进行了定性和定量评价。基于地物光谱特征、解译标志和监督分类法提取试验区土地盐渍化信息,比较多光谱影像和融合影像的土地盐渍化信息提取精度。结果显示,PCA、IHS和HPF融合影像的空间细节表现能力得到提升,而PCA和小波变换融合影像的光谱保真度优于IHS和HPF融合影像;PCA融合影像的盐渍化分类精度、总分类精度和Kappa系数均为最高,是最适于试验区土地盐渍化分类研究的融合方法。
英文摘要	Land salinization is a land degradation phenomenon which deteriorates the eco-environmental quality and agricultural production security, especially in arid and semi-arid areas. Particularly, the land salinization in Hetao irrigation area of Inner Mongolia (including Hangjinhouqi) is a major problem due to the arid climate, high salinity soil material, high mineralized groundwater, as well as high groundwater level caused by improper irrigation and drainage. Therefore, the monitoring of salinized land distribution is significant to prevent land salinization. Fused images based on different spatial and spectral resolutions are an important approach to improve the accuracy of land salinization classification. In this article, ALOS panchromatic and multi-spectral images of central Hanjinhouqi in Inner Mongolia, China, from August, 2010, were fused by employing the four image fusion methods, i.e., principal component analysis transform (PCA), intensity-hue-saturation transform (IHS), high pass filter (HPF) transform and wavelet transform. The effectiveness of each fusion method was evaluated qualitatively and quantitatively to examine the image quality and classification accuracy of land salinization. The result showed that: 1) spatial resolution of images improved after fused by PCA, IHS and HPF transform. 2) Image fused by HPF fused showed higher streaking noise. 3) Edge information of the object in wavelet transform image lowered compared to other fused image. 4) Spectral distortion of the images fused by PCA and wavelet transform was lesser than ones fused by IHS and HPF. In addition, the analysis of spectral signature showed that the mean gray value of different land cover pixel in the study area has the same change trend in the B2 and B3 bands, while different change trend was observed in B4 band because of the vegetation cover. The highest value of mean gray in the B2 and B3 bands was observed in resident cover, followed in sequence by salinized land, cultivated land, traffic land and water body. The highest value of mean gray in the B4 bands was observed in cultivated land .Furthermore, the land cover and land salinization information of researched area was also studied and extracted based on the interpreting marks, spectral signature and supervised classification. The extracted accuracy of multi-spectral images and fused images were compared as well. The classification results showed that the total classification accuracy and Kappa coefficient of PCA image, wavelet PCA image and wavelet IHS image are higher than multispectral images, while IHS image, HPF image and wavelet single band image are lower. The highest and the lowest value of total classification accuracy and Kappa coefficient were determined in PCA and HPF image respectively. The corresponding highest value of total classification accuracy and Kappa coefficient is 89.60% and 0.879 4 respectively while the corresponding lowest value is 65.20% and 0.654 2, respectively. Specifically, the PCA images had the highest classification accuracy of cultivated land (90.30%)and salinized land(90.90%) and HPF images had the lowest classification accuracy of cultivated land (69.23%)and salinized land(62.72%). The evaluation results of fused image quality and classification accuracy showed that PCA fused images is the best image for land use and land salinization information extraction in the study area.
中文关键词	影像融合 ; 土地盐渍化
英文关键词	ALOS ALOS image fusion soil salinization
语种	中文
国家	中国
收录类别	CSCD
WOS类目	GEOSCIENCES MULTIDISCIPLINARY
WOS研究方向	Geology
CSCD记录号	CSCD:5466888
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/232472
作者单位	1.中国地质大学地球科学学院(武汉), 武汉, 湖北 430074, 中国; 2.东北林业大学林学院, 哈尔滨, 黑龙江 150040, 中国
推荐引用方式 GB/T 7714	张利华,翟靖超,李珊,等. ALOS融合影像质量评价及其土地盐渍化应用研究[J],2015,35(6):798-804.
APA	张利华,翟靖超,李珊,&杨金明.(2015).ALOS融合影像质量评价及其土地盐渍化应用研究.地理科学,35(6),798-804.
MLA	张利华,et al."ALOS融合影像质量评价及其土地盐渍化应用研究".地理科学 35.6(2015):798-804.